What are our data sources?
We use the data sources on the side for ranking solutions and awarding badges in document capture software category. Our data sources in document capture software category include;
Most online and offline documents can be categorized as semi-structured data. They are not immediately processable by machines. Initially, template based software attempted to bridge this gap and allow companies to automatically extract data from documents. However, templates enable limited levels of automation and are hard to maintain. Since the last few years, vendors have built machine learning models using millions of sample documents. These models are able to automatically extract data from documents with a high accuracy rate
To be categorized as a document capture software, a product must be able to
How are vendors scored in this category?
Data extraction performance is a key metric for these solutions. We have run a benchmark on the free trial/community edition software. In addition, we asked our clients for similar benchmarks.
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We use the data sources on the side for ranking solutions and awarding badges in document capture software category. Our data sources in document capture software category include;
review websites
social media websites
search engine data for branded queries
According to the weighted combination of 7 data sources
Tipalti
Esker
Veryfi
Docparser
Ephesoft
Taking into account the latest metrics outlined below, these are the current document capture software market leaders. Market leaders are not the overall leaders since market leadership doesn’t take into account growth rate.
Tipalti
Esker
Veryfi
Docparser
Ephesoft
These are the number of queries on search engines which include the brand name of the solution. Compared to other Automation categories, Document Capture Software is more concentrated in terms of top 3 companies’ share of search queries. Top 3 companies receive 57%, 12% more than the average of search queries in this area.
83 employees work for a typical company in this solution category which is 62 more than the number of employees for a typical company in the average solution category.
In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 19 companies with >10 employees are offering document capture software. Top 3 products are developed by companies with a total of 100k employees. The largest company building document capture software is AWS with more than 100,000 employees.
Taking into account the latest metrics outlined below, these are the fastest growing solutions:
Tipalti
Esker
Docparser
Veryfi
Ephesoft
We have analyzed reviews published in the last months. These were published in 4 review platforms as well as vendor websites where the vendor had provided a testimonial from a client whom we could connect to a real person.
These solutions have the best combination of high ratings from reviews and number of reviews when we take into account all their recent reviews.
This data is collected from customer reviews for all Document Capture Software companies. The most positive word describing Document Capture Software is “Easy to use” that is used in 6% of the reviews. The most negative one is “Difficult” with which is used in 3.00% of all the Document Capture Software reviews.
According to customer reviews, most common company size for document capture software customers is 51-1,000 employees. Customers with 51-1,000 employees make up 42% of document capture software customers. For an average Automation solution, customers with 51-1,000 employees make up 31% of total customers.
These scores are the average scores collected from customer reviews for all Document Capture Software. Document Capture Software is most positively evaluated in terms of "Overall" but falls behind in "Likelihood to Recommend".
This category was searched on average for 189 times per month on search engines in 2022. This number has increased to 232 in 2023. If we compare with other automation solutions, a typical solution was searched 1.4k times in 2022 and this decreased to 1.3k in 2023.
Document capture software is an application that can automate the process of scanning paper documents or importing electronic documents for capturing the relevant information for further operations. These tools can collect unstructured forms of data, turn them into actionable information to be used in specific business functions or intents, and store them in databases for future reference.
Here is how document capture software works:
Most common business documents include:
The main benefits include:
To read more about how document capture tools achieve these benefits, feel free to read the related section of our in-depth document capture guide.
Typical document capture use cases include:
For more use cases, you can visit the related section of our in-depth document capture guide.
The ideal document capture tool for your company should:
Extracted data needs to come with confidence scores to enable STP. If scores are not accurate, you may auto process documents that need human in the loop resulting in mistakes or you may require human operators to look at documents that are already extracted correctly
Considering these factors, you should first decide on what kind of document capture tool you need. For example, some vendors can provide better results in handwritten documents while they might not be accurate enough in formatting. Then, you should create a shortlist of possible vendors based on your requirements. Besides software performance, you might also want to consider the following items to make a final decision:
Document capture software leverages the following technologies to perform tasks:
While document capture tools manage a critical part of business operations by handling repetitive, low-skill tasks, the main challenge about these tools is to capture relevant data accurately. While document capture tools can work with high accuracy with typed documents today, they still require human in the loop to avoid any recognition errors.
Yet, active research on machine learning continues to overcome this challenge. Today, this research is mostly focused on handwritten documents and cursive texts, as they are harder to identify. In the future, we expect document capture tools to handle these tasks successfully and without any human intervention. You can read more about this in our current state of OCR technology article.
Besides improving data capture processes, converting unstructured data to structured data is still a developing process. While this process requires AI and machine learning algorithms to structure data accurately, many tools still require human intervention to avoid errors today. Both tech giants like Amazon and startups like Hypatos are investing in machine learning to improve the assignment of text to data entities and therefore converting images more accurately into structured data. As a result, we expect more accurate processes in the future's document capture tools.